1. Field of the Disclosure
This invention relates to data processing and, more particularly, to data mining.
2. Description of the Related Art
Most corporations, including health insurance corporations, maintain a high volume of data. Such data may be analyzed and exploited for valuable information regarding business trends and other important statistics. Data mining is a common strategy for identifying and analyzing such data.
There are many forms of data mining. For example, custom analytic operations may be developed to meet specific needs. Alternatively, commercially-available statistical analysis tools such as Statistical Analysis Software (SAS) may be used to identify statistical trends in data.
Health insurance companies may maintain databases of health insurance claim information, demographic information, and other data about health insurance plan members. Such information may provide valuable insights into disease causes, progressions, and potential cures. Unfortunately, typical methods for analyzing such data are often cumbersome, costly, and require unworkably high processing times and resources.
For example, conventional methods for identifying relationships between independent and dependent variables are limited to specific combinations of variables, such as pairs or triples or higher order combinations of variables. That is, a user must manually identify variables that are important and request a software package to calculate statistics for those combinations of variables. A user is unable to identify unknown statistically-relevant combinations of variables in this manner.
Furthermore, in conventional systems, intervals with an abundance of useful data are masked by surrounding regions of intervals with little useful data. As a consequence, the most relevant and useful data is not identified in conventional systems. Because the data and identified relationships are commonly used as inputs in predictive regressive models, the predictions yielded by these conventional predictive models are sub-optimal.
The referenced shortcomings are not intended to be exhaustive, but rather, are among many that tend to impair the effectiveness of previously-known techniques of disease management; however, those mentioned here are sufficient to demonstrate that the methodologies appearing in the art have not been satisfactory and that a significant need exists for the techniques described and claimed in this disclosure.